@inproceedings{73f682e4e98348e294965eb75d05f404,
title = "Investigating the significance of bellwether effect to improve software effort estimation",
abstract = "Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies have shown an implicit assumption of using recently completed projects (referred to as moving window) for improved prediction accuracy. In this paper, we investigate the Bellwether effect on software effort estimation accuracy using moving windows. The existence of the Bellwether was empirically proven based on six postulations. We apply statistical stratification and Markov chain methodology to select the Bellwether moving window. The resulting Bellwether moving window is used to predict the software effort of a new project. Empirical results show that Bellwether effect exist in chronological datasets with a set of exemplary and recently completed projects representing the Bellwether moving window. Result from this study has shown that the use of Bellwether moving window with the Gaussian weighting function significantly improve the prediction accuracy.",
keywords = "Bellwether Effect, Bellwether moving window, Chronological dataset, Markov chains",
author = "Solomon Mensah and Jacky Keung and MacDonell, {Stephen G.} and Bosu, {Michael F.} and Bennin, {Kwabena E.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 17th IEEE International Conference on Software Quality, Reliability and Security, QRS 2017 ; Conference date: 25-07-2017 Through 29-07-2017",
year = "2017",
month = aug,
day = "11",
doi = "10.1109/QRS.2017.44",
language = "English",
series = "Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security, QRS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "340--351",
booktitle = "Proceedings - 2017 IEEE International Conference on Software Quality, Reliability and Security, QRS 2017",
}